ZFIN ID: ZDB-PUB-190925-9
Deep learning enables automated volumetric assessments of cardiac function in zebrafish
Akerberg, A.A., Burns, C.E., Burns, C.G., Nguyen, C.
Date: 2019
Source: Disease models & mechanisms   12(10): (Journal)
Registered Authors: Akerberg, Alex, Burns (Erter), Caroline
Keywords: CFIN, Cardiac function, Deep learning., Ejection fraction, Light sheet fluorescence microscopy (LSFM), Zebrafish embryos
MeSH Terms:
  • Animals
  • Automation
  • Deep Learning*
  • Embryo, Nonmammalian/diagnostic imaging
  • Embryo, Nonmammalian/physiology
  • Heart/embryology
  • Heart/physiology*
  • Imaging, Three-Dimensional
  • Neural Networks, Computer
  • Reproducibility of Results
  • Zebrafish/embryology
  • Zebrafish/physiology*
PubMed: 31548281 Full text @ Dis. Model. Mech.
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ABSTRACT
Although the zebrafish embryo is a powerful animal model of human heart failure, the methods routinely employed to monitor cardiac function produce rough approximations that are susceptible to bias and inaccuracies. We developed and validated CFIN (cardiac functional imaging network), a deep learning-based image analysis platform for automated extraction of volumetric parameters of cardiac function from dynamic light sheet fluorescence microscopy images of embryonic zebrafish hearts. CFIN automatically delivers rapid and accurate assessments of cardiac performance with greater sensitivity than current approaches.
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